CN1216667A - Process for analysing traffic location in a cellular radio communication network - Google Patents
Process for analysing traffic location in a cellular radio communication network Download PDFInfo
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- CN1216667A CN1216667A CN97193915A CN97193915A CN1216667A CN 1216667 A CN1216667 A CN 1216667A CN 97193915 A CN97193915 A CN 97193915A CN 97193915 A CN97193915 A CN 97193915A CN 1216667 A CN1216667 A CN 1216667A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/18—Network planning tools
Abstract
In order to analyse the location of the traffic of a base station in a cellular radio communications network, the process uses measurements of radio parameters made by mobile stations in respect of the said base station and neighbouring base stations. Statistical processing of these measurements, together with cartographic data on the prameters measured, make it possible to determine traffic density in the cell, at least in areas of high traffic density. These results enable the operator of the network to choose the best locations for the installation of new base stations, and the process is especially suitable for microcells.
Description
The traffic that the present invention relates to base station in the cellular radio communication network is supported is surveyed the process that (the localizationof the treffic) analyzes.
In cellular communications, it is an important problem that the traffic in the cell is surveyed.The network consistency is higher than a certain rank, and microcellulorization is just inevitable.In micro-cellular network, use the short distance base station to form the small-sized honeycomb district (microcellulor or Pico cell) that is included in the large, honeycomb district (umbrella shape cell).This class microcellulor can alleviate umbrella shape cell burden in the big zone of local communication metric density.In fact, the installation of micro-cellular network can realize by microcellulor being added to original relatively large Cellular Networks usually.So the operator has just run into the problem that will determine the optimal zone station to micro cell.
Like this, the operator needs certain can determine the method in the zone that traffic density is big.Usually, the operator comes the big zone of " suppositions " traffic density according to its oneself marketing data, so just micro cell is arranged near the busy street such as commercial center.Practice shows that this empirical method is invalid often.
In the cell of band fan anteena, can analyze the traffic distribution of various sectors, and infer the continuous azimuthal traffic distribution of leap by the rotation processing of sector.The shortcoming of this method is to implement trouble, and the result who provides in the net of city inaccuracy very.
Another kind of possibility is to use simulation base station, and this simulation base station one side moves around in cell, the mobile radio station that the one side statistics attempts to login with it.Though this method may be effectively, needs huge facility, so be difficult to carry out.
An object of the present invention is to provide a kind of method of surveying actual traffic in the cell, instruct the design of radio communication net with the viewpoint that reduces saturation or microcellulorization.
Therefore, the present invention proposes a kind ofly is used for analyzing the method that the traffic that the cellular radio communication network specific base supports is surveyed, each mobile radio station of accepting described specific base service regularly sends to described specific base measures sample, and each measurement sample has comprised the radio parameter value by described moving station measuring, and some neighbor base stations of wherein measuring with respect to approval in described specific base and the one group of predetermined landing (contiguous described specific base) carry out.This process may further comprise the steps:
-compile the measurement sample that described specific base receives;
-handle the measurement sample compile in such a way, promptly produce the sample of p treated data set according to each measurement sample of mobile radio station emission, each data set comprises the identifier of one of neighbor base station in described predetermined group and depends on the measurement data of radio parameter value (being recorded by the neighbor base station of mobile radio station with respect to approval), to the ordering of the data set of each processed sample, making ordering be i (i the maximum wireless electrical parameter values that the group of 1≤i≤p) records corresponding to described relatively group neighbor base station;
-to processed sample classification, for 1 and p between each integer i, each class is associated with a pair of ordering i, and ordering i is to being made of the identifier and the constant interval of certain base station in described predetermined group; For 1 and p between each integer i, be that the paired base station identifier of i is when consistent on the one hand when ordering in the described processed sample is the base station identifier of the data set of i with the ordering that is associated with described classification, processed sample is assigned to this classification, on the other hand, ordering is positioned at the paired constant interval that the ordering that is associated with described classification is i for the measurement data of the data set of i in the described processed sample;
-according to the surveying and mapping data of radio parameter, each region is associated at least with certain classification; And
-according to the sample number that is assigned to relevant classification, estimate the traffic density in the described region.
Therefore, detection operations depends on the processing of the measurement result that mobile radio station is done, and in the provided for radio resources management category, all is essential in most of Cellular Networks in any case this class is measured.Measurement result is caught with real-time mode, constitutes the main body of statistical disposition then, makes it the big zone of identification communication metric density.This method needs surveying and mapping data, and surveying and mapping data can calculate with the ready-made forecasting tool of operator, or measurement is traversing to be determined with experiment by carrying out.
By with reference to the accompanying drawings to the description of non-limiting example, some other characteristics of the present invention as can be seen and advantage.
Fig. 1 is the schematic diagram of cellular radio communication network, shows in addition to be used to implement devices more of the present invention;
Fig. 2 is the overview flow chart of analytic process of the present invention; And
Fig. 3 to Fig. 7 represents in all a kind of as shown in Figure 1 networks the estimation to traffic density.
Fig. 1 illustrates 7 base stations (BTS) 10-16 in certain cellular radio telephone net, and the area of coverage of each base station 10-16 schematically illustrates with hexagon among Fig. 1 as cell C0-C6.
In the following description, suppose that Cellular Networks is a kind of GSM type network.In this class net, each base station all is attached to the functional unit that is called base station controller (BSC), and each BSC can control one or more base stations.Therefore, in the situation of Fig. 1, BSC20 is related with base station 10,14,15.
The surveying work that the present invention has utilized mobile radio station MS to be done, and in GSM net, be used for the control procedure of radio link, the conversion between especially controlling between the cell.In suggestion GSM05.08 (draft pr ETS300578, second edition, 1995.3, European Telecommunications Standards Institute) in detail, relevant surveying work has been described in detail.Each base station provides a inventory to mobile radio station, and this inventory is used to discern the one group neighbor base station of preparation by mobile station monitors.Under the situation of gsm system, the identifier of neighbor base station is made up of broadcast channel (BCCH) number of carrier frequencies and 6 bit-identify sign indicating numbers (BSIC).The base station provides to mobile radio station and is monitored number of carrier frequencies.By the BCCH channel that inspection has a certain frequency in the inventory, mobile radio station is found out corresponding identification code (BSIC).Moving station measuring is from the power level of its serving BS and neighbor base station reception, each power level value is encoded with decibel by 6 (parameters R XLEV), value RXLEV=0 is corresponding to the power less than-110dBm, and value RXLEV=63 is corresponding to the power (seeing suggestion GSM05.08) greater than-48dBm.
Mobile radio station is sent these measurement results back to its serving BS with the 480ms cycle in the channel that is called SACCH (" slow associated control channel "), promptly every 480ms, mobile radio station send to be measured sample (comprising the parameters R XLEV relevant with serving BS), and sends parameters R XLEV and identifier (frequency+BSIC) accordingly for some neighbor base station at least.This measurement sample packages is contained in the message of working out with the GSM term that is called " measurement _ report ".For the radio link control method, the base station is measured sample to these with the message that is called " measurement _ result " and is sent to BSC.
The present invention proposes a kind of statistical method that the information that is included in this class " measurement result " message is handled.Message record unit 21 is contained in the interface for the treatment of between research base station and the BSC thereof (interface A-BIS), is used to check the message of this interface exchange, collects " measurement-result " message that described base station sends.In Fig. 1 example, suppose that base station to be studied is the base station 10 among the cell C0, the contiguous latter of group of base stations, the inventory that sends to serviced mobile radio station comprises the base station 11-16 among the cell C1-C6.Tape deck 21 comprises for example K1103 type protocol tester of Siemens Company's sale.Relevant measurement sample record on mediums such as disk 22.The duration of record is depended on the required measurement sample number N of analyzing communication amount detection.Under the situation of big city cell, need tens thousand of to measure sample usually, the excursion of this sample number mainly depends on the compromise consideration of statistical computation between reliability and complexity.
Shown in the flow chart of Fig. 2, device 21 compiles " measurement-result " message in step 30.Subsequent step 31-36 is then carried out by the calculator 23 of PC one class.
The first step comprises handles 31 to the measurement sample that compiles.For fear of considering because of " in the car " or " indoor " some fade-out that environment caused, the power level RXLEV_NCELL (n) that mobile radio station is recorded with respect to certain contiguous cell, be expressed as with respect to the relative value of this mobile radio station from the field level RXLEV_DL that serving BS records, so, for adjacency n, can limit measurement data CMC (n) by CMC (n)=RXLEV_DL-RXLEV_NCELL (n), parameters R XLEV_DL and RXLEV_NCELL (n) are then by suggestion GSM 05.08 regulation.Measure sample for each, arrange power level with descending order.So, each processed sample j (1≤j≤N) by p data set Id
i(j), CMC
i(j) form (1≤i≤p), wherein Id
i(j) homogeneity (frequency BCCH and BSIC interrelate) of the base station at expression adjacent service station makes the mobile radio station that sends sample record i maximum power level value thus from the contiguous station that is monitored; CMC
i(j) analog value of expression measurement data CMC.
Then treated sample classification.Each classification k with p sort to A
i(k), q
i(k) (1≤i≤p) limit, and each ordering is to comprising the homogeneity A of a base station among the neighbor base station C1-C6
i(k) and index q
i(k) Biao Shi constant interval.For 1 and p between each integer i, index q
i(k) can get Q
iValue is 1,2 ..., Q
i, so Q can be arranged
iIndividual constant interval: [m
i(1), M
i(1) [..., [m
i(Q
i), M
i(Q
i) [.If each is specified treated sample j, then Id from 1 to p value i to classification k
i(j)=A
iAnd CMC (k),
i(j) ∈ [m
i(q
i(k)), M
i(q
i(k)) [.Possible classification number is a great form ([m seemingly
i/ (m-p)! ] xQ
1X ... xQ
p, if m represents the neighbor base station number that is monitored), the classification number that obtains in fact is much smaller, and the major part in the middle of them can be ignored, because they have comprised an inconsiderable sample number.
According to measurement data CMC
i(j) Distribution Statistics is to treated sample j=1 ..., N determines that the right constant interval of i of qualification classification is useful.So in step 32, calculator is determined measurement data CMC to each ordering i
iHistogram, thereby calculate mean value E
iWith standard deviation
i:
In step 33, with these two parameter E
1And σ
iLimit relevant constant interval with ordering i.Three kinds of possibilities can be proposed in this respect:
(ⅰ) mean value is limited Q
i=2 intervals :]-∞, E
i[, [E
i,+∞ [;
(ⅱ) mean value and standard deviation are limited Q
i=3 intervals :]-∞, E
i-σ
i[, [E
i-σ
i, E
i+ σ
i[, [E
i+ σ
i,+∞ [;
(ⅲ) to mean value, standard deviation with considered that the marginal Δ i of decline limits Q
i=3 intervals :]-∞, E
i-σ
i-Δ
i[, [E
i-σ
i-Δ
i, E
i+ σ
i+ Δ
i[, [E
i+ σ
i+ Δ
i,+∞ [.
Certainly, can adopt different section definitions to each i that sorts.Pointed out already that method (ⅲ) can draw optimal results, the general value Δ
iThe power deviation of expression 5～10dB.
In case selected constant interval, calculator 23 is promptly discerned classification k and is added up sample of all categories with regard to the operation of execution in step 34.This step can be implemented N the sample of handling by repetitive instruction.To each sample j that during repetitive instruction, checks, determine whether a classification k of previous identification satisfies Id to each i
i(j)=A
i(k
i) and CMC
i(j) ∈ [m
i(q
i(k)), M
i(q
i(k)) [.If satisfy, before checking next sample, the sample number C (k) that is assigned to classification k is increased a unit.Otherwise, new classification k ' is identified as relevant with current sample, and the sample number C (k ') to its appointment is initialized as 1 before checking next sample.When step 34 finishes, can reject insignificant classification (less than the C (k) of low value).
In step 35, calculator makes the one region be associated with each classification of discerning in step 34 and keep.For realizing this association, calculator itself can be to parameters R XLEV according to surveying and mapping data:
-utilize the ready-made relevant simulation software of operator to calculate;
-or to be measured by test, way is to make one or more receivers, record passes through cell C0 and power level RXLEV in the neighbourhood thereof from what base station 10-16 detected.
If being located at the mobile radio station in a certain place detects from being studied base station and base station A
i(k) (i=1 ..., p) emitted power level and draw CMC
i(k) ∈ [m
i(q
i(k)), M
i(q
i(k)) [, then this place just belongs to the region that is associated with classification k.So certain intersection point that this region that is associated with classification k is exactly all regions has wherein satisfied inequality m respectively
i(q
i(k))≤CMC
i(A
i(k))＜M
i(q
i(k)).
For purposes of illustration, a certain classification k is discussed now, sort 1 pair comprise base station 11 among the cell C1 and interval [18,18[, sorting 2 pairs comprises base station 12 and interval [4 among the honeycomb C2,23[, sort 3 pairs and comprise that [30 ,+∞ [(p=3), then calculates based on the association of surveying and mapping data and can produce region Z1, Z2, the Z3 that represents with hacures in Fig. 3～5 respectively for base station 16 among the cell C6 and interval, the final region that is associated with classification k is Z=Z1 ⌒ Z2 ⌒ Z3, sees Fig. 6.
Related according to the sample number of each classification and classification and region, the traffic density in the relevant region of calculator 23 estimations.Basic communication metric density in the region related with a certain classification, direct proportion is in the area of such other sample number divided by this region in principle.But some place that belongs to some regions may be associated with different classifications, therefore preferably adds the basic density relevant with relevant classification.For ease of explanation, referring now to Fig. 7, if it is related with the classification k of C (k)=2000 sample that area is 40 region Z, if it is related with the classification k ' of C (k ')=1000 sample that area is 50 region Z ', density if region Z and Z ' have an overlapping part Z in " (arbitrary unit); then except region Z " is estimated as the 50+20=70, and the estimation traffic density among the Z of region is 2000/40=50 and be estimated as 1000/50=20 in the Z ' of region.
So, can for example show the traffic density of estimation with the drawing form, allow the operator determine to install the best place of new base station.
As an example, by being 0.23Km at the cell area
2The sub-district in produce the traffic artificially, the method that the applicant can the validating analysis traffic surveys has good performance.Be studied the base station and have m=6 neighbor base station, the neighbor base station that is used for the detection of analyzing communication amount is counted p=3.For each ordering (said method (ⅲ)), adopt constant interval Q
1=Q
2=Q
3=3.Suppose that possible classification number is 3240, and processed the N=81322 of exceeding sample arranged, only observe 365 classifications, wherein deal with have only 20 classifications (account for observation the classification sum 5.5%, representing 91% of total sample number N, other classification is included in the invalid sample number).Implement processing method of the present invention, can observe the traffic as high predicted, the detection accuracy rank of the forecasting tool of application can reach 15 meters in producing the zone of the artificial traffic.
Claims (4)
1. be used for analyzing the method that the traffic that cellular radio communication network specific base (10) supports is surveyed, each mobile radio station (MS) of accepting described specific base service regularly sends to described specific base measures sample, each measurement sample comprises the radio parameter value by described moving station measuring, described measurement is to carry out with respect to some neighbor base stations of described specific base with the middle approval of the one group of predetermined landing (11-16) that is adjacent to described specific base, it is characterized in that described analytical method may further comprise the steps:
Compile the measurement sample that described specific base receives;
Handle the measurement sample that compiles in such a way, promptly produce the sample of p treated data set according to each measurement sample of mobile radio station emission, each data set comprises the identifier (Id of one of neighbor base station in described predetermined group
iAnd depend on (j)) by the measurement data (CMC of mobile radio station with respect to the radio parameter value that records of neighbor base station of approval
i(j)), to the ordering of the data set of each treated sample, making ordering be i (i the maximum wireless electrical parameter values that the group of 1≤i≤p) records corresponding to the neighbor base station of this group relatively;
To the sample classification of handling, for 1 and p between each integer i, each class is associated with a pair of ordering i, and the i that sorts is to the identifier (A by certain base station in described predetermined group
iAnd constant interval [m (k))
i(q
i(k)), M
i(q
i(k)) [) formed, for 1 and p between whole integer i, be that the paired base station identifier of i is when consistent on the one hand when ordering in the described treated sample is the base station identifier of the data set of i with the ordering that is associated with described classification, treated sample is assigned to this classification, on the other hand, ordering is positioned at the paired constant interval that the ordering that is associated with described classification is i for the measurement data of the data set of i in the described treated sample;
According to the surveying and mapping data of radio parameter, each region (Z, Z ') is associated at least with some classification; And
According to the sample number (C (k)) that is assigned to relevant classification, estimate the traffic density in the described region.
2. the method for claim 1 is characterized in that, the described radio parameter that mobile radio station records with respect to the base station is exactly the wireless power level that is received from described base station by described mobile radio station.
3. method as claimed in claim 1 or 2, it is characterized in that, the measurement data (CMC) that is included in the treated sample data group that the measurement sample by mobile radio station emission produces is a difference, the radio parameter value that radio parameter value that promptly described mobile radio station records with respect to the neighbor base station of admitting in the described data set and described mobile radio station record with respect to described specific base poor.
4. as arbitrary described method in the claim 1 to 3, it is characterized in that, for 1 and p between each integer i, be the measurement data Distribution Statistics of the group of i according to ordering in the treated sample, determine that the ordering related with classification is the paired constant interval of i.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| FR9604865A FR2747874B1 (en) | 1996-04-18 | 1996-04-18 | METHOD FOR ANALYZING TRAFFIC LOCATION IN A CELLULAR RADIO COMMUNICATION NETWORK |
| FR96/04865 | 1996-04-18 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN1216667A true CN1216667A (en) | 1999-05-12 |
| CN1134199C CN1134199C (en) | 2004-01-07 |
Family
ID=9491336
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CNB971939152A Expired - Fee Related CN1134199C (en) | 1996-04-18 | 1997-04-15 | Process for analysing traffic location in a cellular radio communication network |
Country Status (13)
| Country | Link |
|---|---|
| US (1) | US5828962A (en) |
| EP (1) | EP0894412B1 (en) |
| CN (1) | CN1134199C (en) |
| BR (1) | BR9708698A (en) |
| CA (1) | CA2252006C (en) |
| DE (1) | DE69727214T2 (en) |
| DK (1) | DK0894412T3 (en) |
| ES (1) | ES2214617T3 (en) |
| FR (1) | FR2747874B1 (en) |
| HK (1) | HK1017965A1 (en) |
| PL (1) | PL183086B1 (en) |
| WO (1) | WO1997039598A1 (en) |
| ZA (1) | ZA973351B (en) |
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-
1996
- 1996-04-18 FR FR9604865A patent/FR2747874B1/en not_active Expired - Fee Related
-
1997
- 1997-04-15 EP EP97918227A patent/EP0894412B1/en not_active Expired - Lifetime
- 1997-04-15 BR BR9708698-3A patent/BR9708698A/en not_active IP Right Cessation
- 1997-04-15 ES ES97918227T patent/ES2214617T3/en not_active Expired - Lifetime
- 1997-04-15 PL PL97329334A patent/PL183086B1/en unknown
- 1997-04-15 CN CNB971939152A patent/CN1134199C/en not_active Expired - Fee Related
- 1997-04-15 DK DK97918227T patent/DK0894412T3/en active
- 1997-04-15 DE DE69727214T patent/DE69727214T2/en not_active Expired - Fee Related
- 1997-04-15 WO PCT/FR1997/000670 patent/WO1997039598A1/en active IP Right Grant
- 1997-04-15 CA CA002252006A patent/CA2252006C/en not_active Expired - Fee Related
- 1997-04-17 US US08/842,815 patent/US5828962A/en not_active Expired - Fee Related
- 1997-04-18 ZA ZA9703351A patent/ZA973351B/en unknown
-
1999
- 1999-07-09 HK HK99102937A patent/HK1017965A1/en not_active IP Right Cessation
Also Published As
| Publication number | Publication date |
|---|---|
| ES2214617T3 (en) | 2004-09-16 |
| US5828962A (en) | 1998-10-27 |
| FR2747874B1 (en) | 1998-07-03 |
| EP0894412A1 (en) | 1999-02-03 |
| FR2747874A1 (en) | 1997-10-24 |
| WO1997039598A1 (en) | 1997-10-23 |
| ZA973351B (en) | 1998-04-20 |
| EP0894412B1 (en) | 2004-01-14 |
| CN1134199C (en) | 2004-01-07 |
| DE69727214D1 (en) | 2004-02-19 |
| CA2252006C (en) | 2004-06-22 |
| CA2252006A1 (en) | 1997-10-23 |
| PL329334A1 (en) | 1999-03-29 |
| DE69727214T2 (en) | 2004-11-04 |
| BR9708698A (en) | 2000-01-04 |
| DK0894412T3 (en) | 2004-05-24 |
| PL183086B1 (en) | 2002-05-31 |
| HK1017965A1 (en) | 1999-12-03 |
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